text:
Keras way of auto-naming layers triggers a naming issues when loading an existing model, and slicing its output

Please make sure that this is a bug. As per our
GitHub Policy,
we only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub. tag:bug_template
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 2.3.1
Python version: 3.7

Describe the current behavior
When adding a strided_slice layer using "numpy-style" syntax (eg [: , :....]), Keras auto-names this layer "tf_op_layer_strided_slice_{i}", incrementing i every time such layer is created.
However, when opening an already-existing model, without creating any layer in the session, i restarts at 0. Hence, when adding at strided_slice operation at the end of the model, an error is triggered as the model now has 2 layers with the same name "tf_op_layer_strided_slice"
PS: i cannot use the explicit tf.strided_slice layer with naming, as it does not have the same "shape-inference" capabilities as numpy-style slicing
Describe the expected behavior
auto-naming shall carry on incrementing from all known layer in the session, whether they have been created or loaded from a model file.
Standalone code to reproduce the issue
Run this code first with initial_builing = True, then False
import tensorflow as tf

initial_builing = False
model_loading = True

if initial_builing:

    layer1 = tf.keras.Input((1,),)
    layer2 = tf.keras.layers.Dense(1)

    model_output = layer2(layer1)[:,:-1]

    model = tf.keras.Model(layer1, model_output)
    model.summary()
    model.save('testmodel')

if model_loading:
    model = tf.keras.models.load_model('testmodel')
    model = tf.keras.Model(model.inputs, model.layers[-1].output[:,:-1])

Other info / logs
Exception has occurred: ValueError
The name "tf_op_layer_strided_slice" is used 2 times in the model. All layer names should be unique.
  File "C:\tests_v2.py", line 19, in <module>
    model = tf.keras.Model(model.inputs, model.layers[-1].output[:,:-1])

I  cannot reproduce this on Colab.

you have to first run with (to build the model)
initial_builing = True
then run a second time (to only load the model)
initial_builing = False

you have to first run with (to build the model)
initial_builing = True
then run a second time (to only load the model)
initial_builing = False

Yes

@scd75
I have tried in colab with TF version 2.3.1 and i am not seeing any issue.Please, find the gist here.Please, correct me if i am doing something wrong.Thanks!

I think that since you are running on jupyter, the variables stay in the cache between the 2 cells. Can you try relaunching the Google colab environment between the 2 executions ? to make sure the model is loaded purely from scratch ?
Here is the full log I get when executing, with the script located in a .py file:
first execution,  with "initial_building" = True

PS C:\Users\xxxxxxxxxxxxxxxxxxxxx>  & 'c:\Users\xxxxxxxxxxxxxxxxxxxxxx\Scripts\python.exe' 'c:\Users\xxxxxxxxxxxxxxxxxxxxx\.vscode\extensions\ms-python.python-2020.9.112786\pythonFiles\lib\python\debugpy\launcher' '52248' '--' 'c:\tests_v2.py'
2.3.1
2020-10-07 09:44:45.381325: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-10-07 09:44:45.460688: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x25f3f56a4e0 initialized for platform Host (this
does not guarantee that XLA will be used). Devices:
2020-10-07 09:44:45.509832: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Model: "functional_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, 1)]               0
_________________________________________________________________
dense (Dense)                (None, 1)                 2
_________________________________________________________________
tf_op_layer_strided_slice (T [(None, 0)]               0
=================================================================
Total params: 2
Trainable params: 2
Non-trainable params: 0
_________________________________________________________________
state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
2020-10-07 09:44:46.091859: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the fuWARNING:tensorflow:From C:\xxxxxxxxxxxx.venv\lib\site-packages\tensorflow\python\training\tracking\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.

second execution, with "initial_building" = False
PS C:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx/.venv/Scripts/Activate.ps1
(.venv) PS C:\Users\xxxxxxxxxxxxxxxxxxxxxxxxxxx\scripts python SC>  cd 'c:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\scripts python SC'; & 'c:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\.venv\Scripts\python.exe' 'c:\xxxxxxxxxxxxxxxxxxxxxx\.vscode\extensions\ms-python.python-2020.9.112786\pythonFiles\lib\python\debugpy\launcher' '52319' '--' 'c:\tests_v2.py'
2.3.1
2020-10-07 09:45:24.173098: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-10-07 09:45:24.196648: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d7ddd78b00 initialized for platform Host (this
does not guarantee that XLA will be used). Devices:
2020-10-07 09:45:24.209542: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.
Traceback (most recent call last):
  File "C:\xxxxxxxxxxxxxxxxxxx\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "C:\xxxxxxxxxxxxxxxxxxx\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "c:\xxxxxxxxxxxxxxxxxxx\.vscode\extensions\ms-python.python-2020.9.112786\pythonFiles\lib\python\debugpy\__main__.py", line 45, in <module>
    cli.main()
  File "c:\xxxxxxxxxxxxxxxxxxx\.vscode\extensions\ms-python.python-2020.9.112786\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 430, in main
    run()
  File "c:\xxxxxxxxxxxxxxxxxxx\.vscode\extensions\ms-python.python-2020.9.112786\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 267, in run_file
    runpy.run_path(options.target, run_name=compat.force_str("__main__"))
  File "C:\xxxxxxxxxxxxxxxxxxx\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "C:\xxxxxxxxxxxxxxxxxxx\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "C:\xxxxxxxxxxxxxxxxxxx\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "c:\tests_v2.py", line 20, in <module>
    model = tf.keras.Model(model.inputs, model.layers[-1].output[:,:-1])
  File "C:\xxxxxxxxxxxxxxxxxxxxxxxxxx\.venv\lib\site-packages\tensorflow\python\keras\engine\training.py", line 242, in __new__
    return functional.Functional(*args, **kwargs)
  File "C:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\.venv\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "C:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\.venv\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 115, in __init__
    self._init_graph_network(inputs, outputs)
  File "C:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\.venv\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "C:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\.venv\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 191, in _init_graph_network
    self.inputs, self.outputs)
  File "C:\xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\.venv\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 942, in _map_graph_network
    str(all_names.count(name)) + ' times in the model. '
**ValueError: The name "tf_op_layer_strided_slice" is used 2 times in the model. All layer names should be unique.**

Ok I've tried with https://github.com/tensorflow/tensorflow/pull/43841 but I don't know if it will have side effects. Let's wait for a review.

@tomerk you can reproduce on Colab with:
!pip install tf-nightly-cpu
import tensorflow as tf
import gc

initial_builing = True
model_loading = True

if initial_builing:

    layer1 = tf.keras.Input((1,),)
    layer2 = tf.keras.layers.Dense(1)

    model_output = layer2(layer1)[:,:-1]

    model = tf.keras.Model(layer1, model_output)
    model.summary()
    model.save('testmodel')
del model, model_output
tf.keras.backend.clear_session()
gc.collect()
if model_loading:
    model = tf.keras.models.load_model('testmodel')
    model = tf.keras.Model(model.inputs, model.layers[-1].output[:,:-1])

Hmm okay that's surprising to me that it still happens in the nightlies, because the name-gen for op layers should follow similar logic as for other layers. I'll take a look.

@tomerk Is name in  that land in  name=_TF_OP_LAYER_NAME_PREFIX + name already autoincrement/unique?

So, I've confirmed that this is a general saving/loading issue with the auto-naming layers whenever adding any layer after loading the model not just when slicing the output. (I repro'd it w/ e.g. another dense layer at the end instead of slicing.)
I'll go ahead and escalate this because this seems like a pretty serious issue.

@tomerk So can I close https://github.com/tensorflow/tensorflow/pull/43841 or do you have any hints for change that PR?

yeah that pr won't fix this problem. We'll have to fix either the save/load code or the backend unique_object_name code or both.

@tomerk Let me know If you have any pinpoint I could try to modify that PR if you are out of bandwidth.

I have a tentative fix ready internally. Once it's submitted it should get synced out to github pretty quickly.

Okay, this should now be working in the nightlies.
The fix in commit 62d414b works by adding any names seen during layer creation to an avoid-list that is used when generating names for auto-gen'd op layers. Note that we don't do this for all layers (even though auto-naming can run into name collisions for other layers as well), because:

it would have the practical affect of changing actual layer names when people do transfer learning w/ a loaded model as a layer in their outer model. This would break a lot of tf1-style name-based checkpoint loading and model manipulation code.
for layers other than tf op layers, users can explicitly pass a name if needed to get around accidental name collisions, so it's not as big of a problem as for auto-gen'd op layers.

@tomerk I've a question. OBSERVED_NAMES in not as PER_GRAPH_OBJECT_NAME_UIDS with Graph key (As PER_GRAPH in the name). Is it ok that it is global?

@tomerk Can you clarify?

The per-graph stuff is primarily legacy-compatibility. The new op layers and the OBSERVED_NAMES set are only used in an eager-first tf2 setting, so it doesn't need special graph-specific logic. A lot of the graph-specific logic in backend has been prone to be fairly buggy and prone to error, which is why I try to avoid it when working on things that aren't needed in tf v1 graph compatibility.

@tomerk Thank you for exposing some history

authors: bhack ravikyram scd75 tomerk


text:
Ability to Save Checkpoints Every N Steps using Estimator

TensorFlow Version: r0.10.0rc0
For TensorFlow/SKFlow/tf,learn Estimators, there is no option for specifying when an Estimator saves checkpoints other than save_checkpoints_secs in a RunConfig, which isn't usable for a monitor such as ValidationMonitor. For example, if I want to do early stopping and check every 10 steps, it is definitely not guaranteed that new checkpoints were saved for the model, even if I specify save_checkpoints_secs to be 1 second.
Tried:
It is currently not possible to use a CheckpointMonitor with an Estimator (e.g. LinearClassifier) as far as I can tell. There is no saver nor Scaffold I can specify that captures the same variables it does normally.
When can we expect this capability, and are there workarounds currently?

@ispirmustafa might have some thoughts on this.

Any thoughts on this?

You can use supervisor_save_model_steps in a RunConfig.

Ok I see that a RunConfig has the save_checkpoints_steps parameter in master but not 0.10 nor 0.11. I'll evaluate if it's worth cloning and building master. Thanks

Thanks for the info @ispirmustafa!
@craymichael I'm closing this out, but feel free to comment or open a new issue if you have further problems.

Is save_checkpoints_steps now in version r1.0? I find it really useful. Thanks.

source: gharchive/issue

authors: craymichael ispirmustafa jinghuangzhu tatatodd

repo: tensorflow/tensorflow

license_type: permissive

license_source: github-api


text:
Unable to build 2.10 from source for GPU due to absl::bit_cast

Click to expand!
Issue Type
Build/Install
Source
source
Tensorflow Version
TF 2.10
Custom Code
No
OS Platform and Distribution
Ubuntu 16.04.7 LTS
Mobile device
No response
Python version
3.9
Bazel version
5.2.0
GCC/Compiler version
11.2
CUDA/cuDNN version
11.3
GPU model and memory
GeForce GTX 1050 Ti
Current Behaviour?
When compiling version 2.10 from source for GPU, I get the attached error after issuing my bazel build command. It looks that the issue is the absl::bit_cast command and the compiler being unable to instantiate the templated functions. The problem maybe with my build setup but I am not able to further debug what exactly is wrong. With exactly the same setup I was able to build from source version the GPU variant of version 2.9.
Standalone code to reproduce the issue
From source, issue:

bazel build --config=cuda \
    --copt=-ftree-vectorize \
    --copt=-fPIC \
    --copt=-fstack-protector-strong \
    --copt=-O2 \
    --copt=-DNO_CONSTEXPR_FOR_YOU=1 \
    --cxxopt=-fvisibility-inlines-hidden \
    --cxxopt=-fmessage-length=0 \
    --host_copt=-DNO_CONSTEXPR_FOR_YOU=1 \
    --linkopt=-zrelro \
    --linkopt=-znow \
    --verbose_failures \
    --config=opt \
    --strip=always \
    --color=yes \
    --curses=no \
    --python_path="${PYTHON}" \
    //tensorflow/tools/pip_package:build_pip_package

### Relevant log output

```shell
external/com_google_absl/absl/base/casts.h(164): error: type name is not allowed

external/com_google_absl/absl/base/casts.h(164): error: identifier "__builtin_bit_cast" is undefined
          detected during instantiation of "Dest absl::lts_20220623::bit_cast<Dest,Source,<unnamed>>(const Source &) [with Dest=uint16_t, Source=int16_t, <unnamed>=0]"

external/com_google_absl/absl/base/internal/endian.h(143): here

external/com_google_absl/absl/base/internal/endian.h(143): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/casts.h(164): error: identifier "__builtin_bit_cast" is undefined
          detected during instantiation of "Dest absl::lts_20220623::bit_cast<Dest,Source,<unnamed>>(const Source &) [with Dest=uint32_t, Source=int32_t, <unnamed>=0]"
external/com_google_absl/absl/base/internal/endian.h(146): here

external/com_google_absl/absl/base/internal/endian.h(146): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/casts.h(164): error: identifier "__builtin_bit_cast" is undefined
          detected during instantiation of "Dest absl::lts_20220623::bit_cast<Dest,Source,<unnamed>>(const Source &) [with Dest=uint64_t, Source=int64_t, <unnamed>=0]"
external/com_google_absl/absl/base/internal/endian.h(149): here

external/com_google_absl/absl/base/internal/endian.h(149): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(153): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(156): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(159): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(233): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(236): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(239): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(243): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(246): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/internal/endian.h(249): error: no instance of function template "absl::lts_20220623::bit_cast" matches the argument list
            argument types are: (<error-type>)

external/com_google_absl/absl/base/casts.h(164): error: identifier "__builtin_bit_cast" is undefined
          detected during:
            instantiation of "Dest absl::lts_20220623::bit_cast<Dest,Source,<unnamed>>(const Source &) [with Dest=const void *, Source=void (*)(int64_t, int64_t, int64_t
, const int64_t *, const int64_t *, const int64_t *, int64_t *), <unnamed>=0]"
./tensorflow/core/util/gpu_kernel_helper.h(108): here
            instantiation of "tensorflow::Status tensorflow::GpuLaunchKernel(void (*)(Ts...), dim3, dim3, size_t, gpuStream_t, Args...) [with Ts=<int64_t, int64_t, int64
_t, const int64_t *, const int64_t *, const int64_t *, int64_t *>, Args=<tensorflow::int64, tensorflow::int64, tensorflow::int64, std::conditional_t<true, int64_t *, con
st int64_t *>, std::conditional_t<true, int64_t *, const int64_t *>, std::conditional_t<false, const int64_t *, const int64_t *>, std::conditional_t<true, int64_t *, con
st int64_t *>>]"
tensorflow/core/kernels/reshape_util_gpu.cu.cc(96): here

17 errors detected in the compilation of "tensorflow/core/kernels/reshape_util_gpu.cu.cc".
Target //tensorflow/tools/pip_package:build_pip_package failed to build
INFO: Elapsed time: 3247.325s, Critical Path: 413.40s
INFO: 27322 processes: 11188 internal, 16134 local.
FAILED: Build did NOT complete successfully
FAILED: Build did NOT complete successfully

Have you already tried to compile with c++17 flags?
https://github.com/tensorflow/tensorflow/blob/cdf6e5f905056c19d591d6c0ce414a100f04f750/.bazelrc#L354-L364
Is -DNO_CONSTEXPR_FOR_YOU still valid?

Yes, I tried removing DNO_CONSTEXPR_FOR_YOU and explicitly enabled C++17, but it is always erroring out at same place. Anywhere GpuLaunchKernel is called the build errors out.
I don't know much about the code base but do the non-GPU builds not use abseil? Because I can build the CPU variant (with and without mkl) just fine, only the GPU variant fails. Is this a problem with nvcc maybe?

I've just compiled master, few days, ago inside the Docker image we use for producing the nightly wheels gcr.io/tensorflow-sigs/build:latest-python3.10 and it compiled without any problem.
Just to understand if it is something related to your environment can you try to reproduce the build with that image?

I can compile inside a container with that image, and I believe it is definitely my environment. But what I am trying to understand is what in my environment is causing this, because I can build 2.9 GPU variant just fine. :(

That's interesting...with GCC 9.3 the compilation is success. Not sure what the problem is, because 11.2 is a newer version after all! But I don't know how I can create a minimum repro example out of this and submit to GCC devs because it is very hard to understand where the bug is.

We had other issues with GCC 11 https://github.com/tensorflow/tensorflow/issues/50303
As the build is only tested with what you see in the table we could have still some compatibility issue (or with our dependencies versions) with other GCC releases.

I see, that is good to know. Thank you for all your assist, I think we can close this issue now unless you want to investigate the root cause. 😀

think we can close this issue now unless you want to investigate the root cause

Thanks, when we will upgrade the official build with new GCC versions probably we will need to check the dependencies versions and eventually their own incompatibility.

authors: babarmehta bhack


text:
[RBM implemention] Problem with GraphDef cannot be larger than 2GB ?

Hi, everyone
I'm trying to implement RBM with tensorflow, here is the code:
rbm.py
""" An rbm implementation for TensorFlow, based closely on the one in Theano """
import tensorflow as tf
import math
def sample_prob(probs):
"""Takes a tensor of probabilities (as from a sigmoidal activation)
and samples from all the distributions"""
return tf.nn.relu(
tf.sign(
probs - tf.random_uniform(probs.get_shape())))
class RBM(object):
""" represents a sigmoidal rbm """
def __init__(self, name, input_size, output_size):
    with tf.name_scope("rbm_" + name):
        self.weights = tf.Variable(
            tf.truncated_normal([input_size, output_size],
                stddev=1.0 / math.sqrt(float(input_size))), name="weights")
        self.v_bias = tf.Variable(tf.zeros([input_size]), name="v_bias")
        self.h_bias = tf.Variable(tf.zeros([output_size]), name="h_bias")

def propup(self, visible):
    """ P(h|v) """
    return tf.nn.sigmoid(tf.matmul(visible, self.weights) + self.h_bias)

def propdown(self, hidden):
    """ P(v|h) """
    return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(self.weights)) + self.v_bias)

def sample_h_given_v(self, v_sample):
    """ Generate a sample from the hidden layer """
    return sample_prob(self.propup(v_sample))

def sample_v_given_h(self, h_sample):
    """ Generate a sample from the visible layer """
    return sample_prob(self.propdown(h_sample))

def gibbs_hvh(self, h0_sample):
    """ A gibbs step starting from the hidden layer """
    v_sample = self.sample_v_given_h(h0_sample)
    h_sample = self.sample_h_given_v(v_sample)
    return [v_sample, h_sample]

def gibbs_vhv(self, v0_sample):
    """ A gibbs step starting from the visible layer """
    h_sample = self.sample_h_given_v(v0_sample)
    v_sample = self.sample_v_given_h(h_sample)
    return  [h_sample, v_sample]

def cd1(self, visibles, learning_rate=0.1):
    " One step of contrastive divergence, with Rao-Blackwellization "
    h_start = self.propup(visibles)
    v_end = self.propdown(h_start)
    h_end = self.propup(v_end)
    w_positive_grad = tf.matmul(tf.transpose(visibles), h_start)
    w_negative_grad = tf.matmul(tf.transpose(v_end), h_end)

    update_w = self.weights.assign_add(learning_rate * (w_positive_grad - w_negative_grad))

    update_vb = self.v_bias.assign_add(learning_rate * tf.reduce_mean(visibles - v_end, 0))

    update_hb = self.h_bias.assign_add(learning_rate * tf.reduce_mean(h_start - h_end, 0))

    return [update_w, update_vb, update_hb]

def reconstruction_error(self, dataset):
    """ The reconstruction cost for the whole dataset """
    err = tf.stop_gradient(dataset - self.gibbs_vhv(dataset)[1])
    return tf.reduce_sum(err * err)

######################################################################
rbm_MNIST_test.py
import tensorflow as tf
import numpy as np
import rbm
import input_data
def build_model(X, w1, b1, wo, bo):
h1 = tf.nn.sigmoid(tf.matmul(X, w1)+b1)
model = tf.nn.sigmoid(tf.matmul(h1, wo)+bo)
return model
def init_weight(shape):
return tf.Variable(tf.random_normal(shape, mean=0.0, stddev=0.01))
def init_bias(dim):
return tf.Variable(tf.zeros([dim]))
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])
rbm_layer = rbm.RBM("mnist", 784, 500)
for i in range(10):
print "RBM CD: ", i
rbm_layer.cd1(trX)
rbm_w, rbm_vb, rbm_hb = rbm_layer.cd1(trX)
wo = init_weight([500,10])
bo = init_bias(10)
py_x = build_model(X, rbm_w, rbm_hb, wo, bo)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)
predict_op = tf.argmax(py_x, 1)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
for i in range(10):
for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
print i, np.mean(np.argmax(teY, axis=1) ==
sess.run(predict_op, feed_dict={X: teX, Y: teY}))
################################################################
but here comes the error:
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1626, in as_graph_def
raise ValueError("GraphDef cannot be larger than 2GB.")
ValueError: GraphDef cannot be larger than 2GB.
Can someone help me to solve?

Looks like @keveman answered this question on StackOverflow, so I'll close this issue.

authors: btpeter mrry


text:
GPU use

Issue type
Bug
Have you reproduced the bug with TensorFlow Nightly?
Yes
Source
source
TensorFlow version
2.10
Custom code
Yes
OS platform and distribution
Window
Mobile device
No response
Python version
3.9
Bazel version
No response
GCC/compiler version
No response
CUDA/cuDNN version
12.5/9.2.1
GPU model and memory
No response
Current behavior?
hey i successfully setup cuda and able to use GPU in WSL , but my question is how i use it on my windows Conda Environment
Standalone code to reproduce the issue
hey i successfully setup cuda and able to use GPU in WSL , but my question is how i use it on my windows Conda Environment

Relevant log output
hey i successfully setup cuda and able to use GPU in WSL , but my question is how i use it on my windows Conda Environment

install this
https://mega.nz/file/1vkGlJTL#yU-SJLlIilF0mx5qwpPOd3BM8iKZqOJLEQUStR76rrQ
you may need to install the c compiler

my cuda version is 12.6

Hi @Tarun0000 ,
Thank you for your confirmation. Your CUDA version should be 11.2, and the cuDNN version should be 8.1 for TensorFlow 2.10. I have provided the documentation for your reference.
Thank you!


text:
Android Example Doesn't Build: Could not get unknown property 'assembleRelease'

What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?
http://stackoverflow.com/questions/39602587/could-not-get-unknown-property-assemblerelease-for-project/39720618
http://stackoverflow.com/questions/39590549/after-update-to-android-studio-2-2-gradle-plugin-2-2-0-could-not-get-unknown
Environment info
Operating System:
Ubuntu 16.10
Installed version of CUDA and cuDNN:
Toolkit 8.0.1 cuDNN 5.1
If installed from binary pip package, provide:
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.1-cp36-cp36m-linux_x86_64.whl
1.0.1
If possible, provide a minimal reproducible example (We usually don't have time to read hundreds of lines of your code)
The default android example code. Specifically build.gradle
When trying to run the code on an emulator, for example, there is a config error:
Could not get unknown property 'assembleRelease for root project 'android' of type org.gradle.api Project (Get this twice)
Links to:
afterEvaluate {
    assembleDebug.dependsOn copyNativeLibs
    assembleRelease.dependsOn copyNativeLibs
}

from build.gradle lines 143-146
What other attempted solutions have you tried?
It looks like this is a conflict with gradle of some sorts. My knowledge is minimal with gradle/adroid studio. Some online threads say it should be included in a task but when putting
task afterEvaluate{ ...} there is a similar error and this doesn't work either.
This post from a googler implies that your code is written as it should be though.
https://code.google.com/p/android/issues/detail?id=219732#32
At my wits end with this. For reference, I am able to build with bazel from the terminal. This seems to be a gradle/AS induced issue.
Logs or other output that would be helpful
Information:Gradle tasks [:assembleDebug]

Error:(145, 1) A problem occurred configuring root project 'android'.
> Failed to notify project evaluation listener.
   > Could not get unknown property 'assembleRelease' for root project 'android' of type org.gradle.api.Project.
   > Could not get unknown property 'assembleRelease' for root project 'android' of type org.gradle.api.Project.

What version of Gradle are you using? This should work with 2.3.0

@andrewharp Gradle Version 3.3 but Android Plug-In version 2.3.0 (which I assume is what you meant). This is a fresh version of AS on a fresh OS with zero tinkering so not sure why I can't get it to work.
For reference I was just following the tutorial. i.e. Open project, Run (on emulator in this case). I did make one change to force sensor orientation to 90 as it runs at 3 deg on my pixel C but that's it.
I will try again without that change and start from scratch to see if it works again.

Same issue.
FAILURE: Build failed with an exception.

Where:
Build file '/Users/tensorflow/tensorflow/examples/android/build.gradle' line: 144

What went wrong:
A problem occurred configuring root project 'android'.

Failed to notify project evaluation listener.
Could not get unknown property 'assembleRelease' for root project 'android' of type org.gradle.api.Project.
Could not get unknown property 'assembleRelease' for root project 'android' of type org.gradle.api.Project.

@ggfan I think this may be related to 360f449d95cf487fd35dbcbc548a6b65fa7ae64f

@shyzh @jubjamie Did this ever build before? I'm not able to reproduce with a fresh pull.
Maybe try rm -rf gradleBuild/ gradlew gradlew.bat gradle to make sure you don't have incompatible version of gradle cached somewhere then try building again?

@andrewharp Not in the office atm but I've never built it before. Brand new OS, build etc. So shouldn't be a cache issue. Never even used gradle before. And I've been doing fresh clones from git in separate directories each time so doubt that will fix. Can try it tomorrow though.

@andrewharp Sorry for double posting but gradle cache doesn't seem to be the issue.
EDIT: Strangely, I had the problem again once. Then the next time it built correctly (android_debug.apk). I will try and reproduce this but I really have no idea why sometimes it builds and sometimes it doesn't. Will report back.

I have had the same issue and this seems to help fix it.
    tasks.whenTaskAdded { task ->
        if (task.name == 'assembleDebug') {
            task.dependsOn 'extractModels'
        }
        if (task.name == 'assembleRelease') {
            task.dependsOn 'extractModels'
        }
    }

I'm having the same error as @jubjamie .  @sam-trt adding that block to my build.gradle file didn't seem to change anything, by adding it like so:

@nick-jonas sorry you need to change it to look like this
assemble.dependsOn copyNativeLibs
afterEvaluate {
tasks.whenTaskAdded { task ->
if (task.name == 'assembleDebug') {
task.dependsOn 'copyNativeLibs'
}
if (task.name == 'assembleRelease') {
task.dependsOn 'copyNativeLibs'
}
}
}
// Download default models; if you wish to use your own models then
// place them in the "assets" directory and comment out this line.
apply from: "download-models.gradle"

@sam-trt I now get the following error with download-models.gradle:

@nick-jonas you'll need to make an equivalent change in download-models.gradle too (altering it to take I to account the different targets).

I think @sam-trt solution works and matches some similar issues I spotted in my searches online. I'm getting an INSTALL_FAILED_NO_MATCHING_ABIS error now which I believe is to do with CPU architecture etc. and unrelated to this issue. @sam-trt and @andrewharp would you like me to pop this in as a PR?

@jubjamie I haven't seen the INSTALL_FAILED_NO_MATCHING_ABIS error before -- can you describe where you're seeing that and what the fix was?
I've made a change internally to apply @sam-trt's fix to download-model.gradle and build.gradle; should be pushed within a day or two.

Here is build procedure I used on Ubuntu:

Using Android Studio 2.3.0,  import the project ( file --> new --> import (Select Eclipse or Gradle Project to import) --> example/android/build.gradle

Let Android Studio to create wrapper for the project ( click OK)
in build.gradle, change "def buildWithMake = true"  ( this should not matter too much: bazel build is the same )
make sure this one works:  def makeNdkRoot = System.getenv('NDK_ROOT'); otherwise I use android studio's ndk version [ inside local.properties file, copy NDK.DIR setting to it ]
Build --> Build APK.

there are some error messages coming from make build, but it could be ignored and APK is built on my system.

Ok, I see it is IDE "run" button causing the trouble: need to remove assembleRelease.dependsOn when building for debug ( vice versa ).  So just comment that line would be fine, a patch pretty soon to fix it.

@ggfan Building the APK isn't an issue, it was running through the IDE as you pointed out. Building with Bazel is fine and not causing the problem so no need for step 3 (however I guess it's personal preference? Or is one method preferred?)
@andrewharp the INSTALL_FAILED_NO_MATCHING_ABIS error is caused when your app has native libraries but none for the cpu architecture that you are installing on. I see that in build.gradle there is a place to change the CPU architecture so I will try building with that changed. However, by the looks of it, it will overwrite the previous APK and libraries?
It's not a TF issue but more of a Android development thing. Nonetheless I would suggest it was mentioned in the docs a bit clearer for Android Studio. I'm happy to make those edits.

@jubjamie Right, if building with Gradle you need to manually select the right ABI. If running in an emulator, for example, typically you'll want to select x86 or x86_64.
It's possible to use bazel to build for multiple ABIs at a time with the --fat_apk_cpu flag. android_full.sh (which is how we build the nightly APK) passes --fat_apk_cpu=armeabi-v7a,arm64-v8a,x86,x86_64
Contributions welcome to replicate the multi-ABI functionality in build.gradle. It would probably require invoking bazel multiple times, once for each ABI being built, as I believe the --fat_apk_cpu only works when building android_binary targets.

@andrewharp yeah I did notice the flag in the gradle file. I haven't had a big play around with building for multiple targets as I wanted to get in Android Studio to help me understand the code etc.
I am a newbie to a lot of this so I might not jump at the multi-ABI in gradle task but if I come across needing to do it in the next couple of weeks then I can add that to my android pull request.
I noticed in the 1.1 release notes that cmake building is becoming available soon so will this impact cmake building too?

@jubjamie The bazel-built APK from android_full.sh will work on all 4 of those architectures.
If you want to simulate this with the Gradle+bazel build, you can manually change the cpuType variable in build.gradle to each in turn and hit build. The built native libraries will accumulate in the jniLibs directory, and Gradle will package them in the resulting APK. You can see what libraries actually get packaged with unzip -v tensorflow/examples/android/gradleBuild/outputs/apk/android-debug.apk
However be aware that making any changes to the native code will require a full repeat of the entire process, as otherwise your native libraries will become out of sync with each other. This is why it would be good to have build.gradle itself automate the process.

@andrewharp so if I build the APK 4 times after changing the cpu type, all of them will get packaged because the binaries (or whatever) accumulate in the Libs folder etc? Because if so then looping gradle automatically might not be too tricky for me to have a stab at.
Re: cmake. Ok. I was hoping I could get it working on Windows with make. I feel like I hit some errors before. Might give it another go now i'm more clued up. Thanks

@jubjamie
Yes, that's exactly how it would work if you want to take a crack at it :)
Unfortunately the make build doesn't work on Windows out of the box. This is the current easiest way I know of to build Android TensorFlow on Windows. It should be approximately the same amount of effort to use this approach for either make or bazel builds. Hopefully we get cmake working in the near future so we can avoid the extra steps (contributions welcome!)

@andrewharp I am definitely not knowledgeable enough  to help with cmake stuff but I might give the gradle bits a go if I find some time in the next week or so. When/If I have a go I will add it my currently open Pull Request (or make a new one) and we can continue the conversation there.
Thanks for your help with this particular issue and android in general. I'm still struggling to understand how most of it works so I appreciate having this communication. Source code is hard when you didn't write it :(
Thanks!

authors: andrewharp ggfan jubjamie nick-jonas sam-trt shyzh


text:
libtensorflow_cc.so on Android

Hello again team TF!
After revisions, I made a more stable solution for building libtensorflow_cc.so on android that should avoid breaking common build scenarios. There may be issues if attempting to build for non-android armeabi-v7a or arm64-v8a, but I'm unable to confirm.
I found issues with android NDK r15 and bazel 0.11.0, forcing an update to NDK r16 and bazel 0.12.0 to ensure a working android build. As part of this, I also made a switch to the new libc++ version of STL in android for the tensorflow build, which does not suffer from the near proximity to GPLv3 with gnustl_static/shared.
I am not certain, but it seems like the default --config=android chain still uses gnustl libraries, which are not fully C++11-compatible and due to be deprecated in the android NDK.
A terrifying build command is still being used:
bazel build -c opt --config=monolithic --android_crosstool_top=@androidndk//:toolchain-libcpp --crosstool_top=@androidndk//:toolchain-libcpp --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cpu=$ARCH //tensorflow:libtensorflow_cc.so --cxxopt="-std=c++11" --copt="-DMDB_USE_ROBUST=0" --cxxopt="-DTENSORFLOW_DISABLE_META" --cxxopt="-DEIGEN_HAS_C99_MATH" --cxxopt="-Wno-c++11-narrowing" --copt="-DS_IREAD=00400" --copt="-DS_IWRITE=00200" --verbose_failures --copt="-DABSL_BASE_POLICY_CHECKS_H_" --copt="-DPNG_ARM_NEON_OPT=0"
Most of these are to avoid build issues within libraries that do not have specific checks in place for android toolchains. I presume the build command could be shortened by introducing the commands into the android_cc flag and the specific libraries in question, but it's out of scope for my case right now.
Some of these flags may also have been resolved by using @androidndk//:toolchain-libcpp, especially the need for TENSORFLOW_DISABLE_META, EIGEN_HAS_C99_MATH and MDB_USE_ROBUST, and DS_*, but I did not have time to test all variants.
To enable the C++ API, I created a new config, android_cc, which is checked for in all applicable places. The - beyond all others - most common fault is -lpthread being set in various files and libraries. This may be possible to resolve by instead using "-pthread", which is reportedly a flag for specifying pthread inclusion in a platform-compatible manner.
Further, I had to add build files for gemmlowp, protobuf_archive and boringssl, to correct similar flag issues. Ideally, these should be resolved in the respective packages.
A remaining problem is that tensorflow is currently linking the static android STL library versions. I could not find a way to resolve this with bazel. Fortunately it does not seem to be visible externally, so other libraries linked with c++_shared/static still work.

Nagging Reviewer @angersson: It has been 20 days with no activity and the awaiting review label was assigned. Can you please take a look?

Nagging Reviewer @angersson: It has been 36 days with no activity and the awaiting review label was assigned. Can you please take a look?

Nagging Reviewer @angersson: It has been 51 days with no activity and the awaiting review label was assigned. Can you please take a look?

@aselle @gunan can you look at this? I don't think I have the expertise to review appropriately.

@linusmartensson thanks for your change!
However, This is a PR to 1.8 branch, and old release branch.
This change will be lost in this old release.
Could you recreate this change to the master branch?
As a policy, we do not accept changes to any release branches, except for critical bugfixes.

Nagging Assignee @martinwicke: It has been 15 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @martinwicke: It has been 31 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

I'll close this. Please reopen against master.

source: gharchive/pull-request

authors: angersson gunan linusmartensson martinwicke tensorflowbutler


text:
tv.Variable doc: fix typo

'b' does not exist in the example, probably 'y' was meant.

Can one of the admins verify this patch?

Can one of the admins verify this patch?

I signed it!

I signed it!

authors: da-x gunan tensorflow-jenkins


text:
[ROCm] Fix for the broken ROCm CSB.

This PR addresses to separate breaks in ROCm CSB
1
The following commit breaks the --config=rocm build
ab65243
The commit above introduces the test "test_opt_einsum_cached" in //tensorflow/python:special_math_ops_test_gpu
The order of execution of other tests within that file can dictate whether or not the newly added test will pass or fail.
The failure (caught byt he ROCm Nighty CSB run) does not seem specific to the ROCm platform.
The "fix" is to explicitly clear the lru_cache of the routine "special_math_ops._get_opt_einsum_contract_path" (before running the test) to gurantee that the test will pass, irrespective of the order in which it is run relative to the other tests.

2
The following commit breaks the --config=rocm build
c8b0100
The commit above introduces the test "testFusedBatchNormGradsInference" in //tensorflow/python/eager:forwardprop_test_gpu
We are still working towards analysing the cause of the failure and potentially coming up with the fix. In meantime, the change in this commit is to skip the failing subtest on the ROCm platform. This is so that we can get he ROCm Nightly CSB build passing again.

@whchung @chsigg

@chsigg , please re-approve. I pushed out a new commit to fix the py_lint error. thanks

@chsigg, gentle ping

@gbaned gentle ping


text:
[ROCm] Reduction for complex types, dynamic warp setting

This PR supplies two related sets of ROCm-related changes:

Reduction for complex data types
Dynamic warp size setting (on ROCm, warp size is either 32 vs 64 depending on the GPU and the value is not known at compile time)

gentle ping

@chsigg @cheshire gentle ping

@ekuznetsov139  Can you please check @cheshire's comments and keep us posted ? Thanks!

@ekuznetsov139 Any update on this PR? Please. Thanks!

I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen!

authors: deven-amd ekuznetsov139 gbaned


text:
[tf.data] graduate experimental save and load APIs to tf.data

UPDATED: This PR graduates the tf.data.experimental.save and tf.data.experimental.load APIs into tf.data.Dataset by making the following changes:

[x] Adds the deprecation decorator for the experimental API and export the new API.
[x] Adds the necessary save and load methods to DatasetV2 class.
[x] Updates example in documentation with new API.
[x] Regenerate golden API's.
[x] Moved and updated the io_test target from experimental/kernel_tests to kernel_tests
[x] Updated the RELEASE.md file

TEST LOG
INFO: Build completed successfully, 349 total actions
//tensorflow/python/data/kernel_tests:io_test                            PASSED in 1.9s

cc: @jsimsa

@kvignesh1420 upon further review, I think we need to let these operations incubate in experimental longer. In particular, the operations do not support checkpointing their progress, which is something that should be implemented before graduating them out of the experimental namespace.

@kvignesh1420 Can you please resolve conflicts? Thanks!

@jsimsa I see. We can come back to this after we graduate the other APIs then. Will close this PR as of now.

authors: gbaned jsimsa kvignesh1420


text:
Update flatbuffers in workspace.bzl

Updated Flatbuffers to "2.0" Uas dependency change to "https://github.com/tensorflow/tensorflow/pull/51504"

Flatbuffer 2.0 has some incompatible changes that we need to resolve from internally first. It will be difficult to debug and test from external contribution. Therefore I'll reject this PR for now, and we'll do the upgrade later.


text:
Branch 141580230

Pushing internal changes

The half_plus_two rules are referenced also from:
tensorflow/cc/saved_model/BUILD  loader_test rule
tensorflow/python/saved_model/BUILD  saved_model_test
tensorflow/contrib/session_bundle/BUILD  bundle_shim_py_test session_bundle_test session_bundle_py_test rules.

@andrewharp the current test failures can be seen in your branch.
extract function is defined twice in tensorflow/contrib/learn/python/learn/estimators/estimator_test.py
maybe a merge conflist resolution issue?

Once the tests pass, good to go

The build "Linux CPU Tests" failed because several tutorials in tensorflow/models have been removed. This is not a big deal - we can fix it easily by removing them from test_tutorials.sh.
The "Linux GPU" build failed due to two timeouts:
//tensorflow/python/kernel_tests:fft_ops_test
//tensorflow/python/kernel_tests:shape_ops_test
The first one looks like an existing flaky timeout. The second one may be related to this push? In any case, I think we should move ahead and merge this pushing PR. @gunan @andrewharp

If I had to guess, the Windows cmake failure is also due to half_plus_two_data being missing: https://github.com/tensorflow/tensorflow/pull/6223/files#diff-79d31bc9f4560bd3b40a78be48d3a228

authors: andrewharp caisq gunan


text:
feature request: toPixels

Right now deeplearn.js has a method fromPixels which takes an ImageData and converts it into a Tensor3D.
It would be nice to have something that does the inverse, like toPixels - which would take a 2d or 3d tensor and convert it to an ImageData

This issue was moved to tensorflow/tfjs#103

authors: nsthorat oveddan

repo: tensorflow/tfjs-core

license_source: bigquery


text:
Add tf.addN(), allow bool weights and improve tslint

Add tf.addN() following tf.add_n in Python. This is needed when converting the ssd_mobilenetv2_coco model.
Add ability to load weights of dtype bool
Improve tslint rule so imports {} and exports {} are not subject to 80 width line length
Also fix circular dep which was disabled on some files due to //tslint:disable

FEATURE

This change is

Will submit since the Travis failure is unrelated (one of the browsers got disconnected)


text:
#0: Don't error on unused functions in compiler call

Issue comes up after sfpi bump I think, in case where we use TT_METAL_WATCHER_NOINLINE. Figured it's fine to just not error on this, but let me know if we should investigate further.
CI: https://github.com/tenstorrent/tt-metal/actions/runs/9321544881

CI looks good, will merge
https://github.com/tenstorrent/tt-metal/actions/runs/9321544881/job/25660900845

repo: tenstorrent/tt-metal


text:
Add missing concepts for suricata events

:notebook_with_decorative_cover: Description

:memo: Checklist

[x] All user-facing changes have changelog entries.

Is this exempt from the feature freeze? Should we merge this and add an rc2?

In my opinion this is very low risk, but I would not want to force an rc2 just for this.

Then let's only merge this if we need another mixup.

authors: dominiklohmann tobim

repo: tenzir/vast


text:
method locator "getByLabel" not working

Hello,
The method locator getByLabel still does not work
$page->getBylabel('my "label');

returns
          {
            '_selector' => 'internal:label="my \\"label"i',
            '_frame' => {
                          '_type' => 'Frame',
                          '_guid' => 'frame@9f71c8e230fd7e5b6bce37ad7f2c21ae'
                        }
          }

did not test the others, getByAltText|getByTitle|getByPlaceholder|getByText|getByRole but, i think they have same problem :)
thanks.

Makes sense.  I suspect I'll need to add handlers for all the methods on Page which return Locator objects for the same reason.

Needs to happen on Frame objects as well, and return FrameLocator instead of locator in that case too.

Found a way to get them blessed correctly for everything returning Locator objects, so hopefully this handles all the new locator methods they add in the future too.  Will go up to CPAN shortly.

authors: teodesian whity

repo: teodesian/playwright-perl

license: MIT


text:
Code to find dual equations

Fixes #72.
It might be nice to be able to also produce a proof of the duality of each of these equations in Lean. Right now you have to trust in my (and Claude's) coding ability. I believe it to be correct but that's not saying much.

Once we have #36 we should be able to formalize duality in Lean, but for now a numerically generated list should be fine.  We can at least numerically test whether various numerically generated sets of implications are duality-invariant, which presumably they should be if they were generated by a duality-invariant scheme.

authors: carlini teorth


text:
Fixing typo

Proposing typo fix from "An common gulpfile example" to "A common gulpfile example".

Merged as 14f1d7190fc94efd6f29b2e16957418bbf1a3adf.

authors: eowino terinjokes

repo: terinjokes/gulp-uglify

license: mit


text:
Use Native Unicode Converter

Use JavaScript's native function to return the full character code

So, I just thought this would be easier...is it faster I guess to do it the other way?

Who would have thought that would be faster than JavaScript's native function though??

We're targeting ECMAScript 3 as minimum environment, which doesn't have codePointAt.

But you're not using ES3 anywhere else in the code? You're using let and stuff, and that's not in ES3. And you're transpiling it anyway, so what does it matter?

@zwhitchcox It's just source code, we're anyway to valid ES3. What you're suggesting is not about transpilation, it's new API that is unavailable in ES3 browsers.

Who would have thought that would be faster than JavaScript's native function though??

In fact, in many cases handcrafted code is faster than built-ins (starting from Math.max / Math.min and ending with more complex ones), simply because built-ins are specified as very generic and need to do lots of additional checks.

Ok, gotcha. So, it's part of the polyfill and not actually transpiled?

And I guess we don't want to include the polyfill?

Except it will be in the code after bundling, and will be likely bigger than "polyfill" we already have here. No wins compared to current version, only downsides.

If left-pad story teaches anything, it's that it's unreasonable and even dangerous to rely on 3rd party implementation of a few-liner when you can do it yourself (and likely better for your specific case).

authors: RReverser marijnh zwhitchcox

repo: ternjs/acorn


text:
Package import autocompletion causes duplicate quotes

Using any autocompletion of a node module installed with npm causes the quotes used to be duplicated.
Example:

This also occurs for imports of the form const React = require('react').

Yes same issue:

Yes,  same issue:

anyone working on this?

Can confirm this issue also

yeap i'm in on this too...any solution in sight ?

My current solution was switching to VS Code for the better eco system. It has built-in code analyzing/intelligence for Typescript which can be enabled for Javascript as well with
{
  "javascript.implicitProjectConfig.checkJs": true
}

I still use ST for its speed though and would often prefer it, but too many plugin devs switched to Code. And Code definitely has its benefits.

A quick fix for this is to remove any ' by adding .replace("'", "") to the end of https://github.com/ternjs/tern_for_sublime/blob/91a27a39b1b0a33a9043aa685e1ee48c64a58274/tern.py#L438

A quick fix for this is to remove any ' by adding .replace("'", "") to the end of
tern_for_sublime/tern.py
Line 438 in 91a27a3
rec_name = rec.get('name').replace('$', '\$')

This only removes the single quotes but still adding the extra '../' It should be replacing what you typed in quotes completely.

Any news on this issue? It is incredibly annoying..

authors: TheReturningVoid ViggoV berkandirim codeHamster9 davae1an easyfrog philipbordallo simonhaenisch


text:
feat: auth module accounts api

Fixes the following issue https://github.com/terra-money/feather.js/issues/9

Codecov Report

Merging #30 (9f06cd8) into main (fd09339) will increase coverage by 0.08%.
The diff coverage is 69.64%.

@@            Coverage Diff             @@
##             main      #30      +/-   ##
==========================================
+ Coverage   57.57%   57.65%   +0.08%
==========================================
  Files         205      206       +1
  Lines        8167     8223      +56
  Branches      608      615       +7
==========================================
+ Hits         4702     4741      +39
- Misses       3429     3446      +17
  Partials       36       36

Impacted Files
Coverage Δ

src/core/auth/Account.ts
31.70% <40.00%> (+1.15%)
:arrow_up:

src/core/auth/ModuleAccount.ts
69.56% <69.56%> (ø)

src/client/lcd/api/AuthAPI.ts
100.00% <100.00%> (ø)

src/core/index.ts
100.00% <100.00%> (ø)

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


text:
Handle ledger status code 27404

Unknown Status Code: 27404 appears when, while trying to sign a transaction, the ledger is on the screensaver and needs to be unlocked

This codes will be replaced to terra-money/ledger-terra-js
And your changes will be added here: terra-money/ledger-terra-js#2

repo: terra-money/station


text:
Create multiple VMs in a single API call

Want to create multiple VMs in a single placement group in a single API call.

please see https://github.com/terraform-aws-modules/terraform-aws-ec2-instance/blob/master/examples/complete/main.tf#L209 for reference


text:
docs: Incorrect default in description of desired_count

In the service module, the variable description below is incorrect considering the default value.
Note, "Defaults to 0" could probably be removed entirely, as the default value is displayed in Terraform docs.
variable "desired_count" {
  description = "Number of instances of the task definition to place and keep running. Defaults to `0`"
  type        = number
  default     = 1
}

This issue has been resolved in version 5.6.0 :tada:

authors: antonbabenko baolsen

repo: terraform-aws-modules/terraform-aws-ecs


text:
irsa example not working

Description
Followed the readme to the letter, have also tried an earlier version of the helm chart for cluster-autoscale
First i was getting failed to list errors in autoscalar pod logs:
Listing and watching *v1.PodDisruptionBudget from k8s.io/client-go/informers/factory.go:134
E0727 20:55:20.046630       1 reflector.go:138] k8s.io/client-go/informers/factory.go:134: Failed to watch *v1.PodDisruptionBudget: failed to list *v1.PodDisruptionBudget: the server could not find the requested resource
I0727 20:55:20.123857       1 reflector.go:255] Listing and watching *v1beta1.CSIStorageCapacity from k8s.io/client-go/informers/factory.go:134
E0727 20:55:20.126729       1 reflector.go:138] k8s.io/client-go/informers/factory.go:134: Failed to watch *v1beta1.CSIStorageCapacity: failed to list *v1beta1.CSIStorageCapacity: the server could not find the requested resource
Then i added the CNI-vpc plugin and now i get this:
aws_manager.go:265] Failed to regenerate ASG cache: cannot autodiscover ASGs: WebIdentityErr: failed to retrieve credentials
caused by: ValidationError: Request ARN is invalid
status code: 400, request id: 2e741913-bd68-441b-95c5-13a8299180df
I have validated that the ARN im using in the cluster-autoscaler-chart-values has the correct id and this looks correct (im actually templating it using "arn:aws:iam::$(terraform output -raw aws_account_id):role/cluster-autoscaler"
I cant get through to AWS support, would really appreciate help!
Versions

Terraform:
Terraform v1.0.2
on darwin_amd64

Provider(s):

provider registry.terraform.io/hashicorp/aws v3.51.0
provider registry.terraform.io/hashicorp/cloudinit v2.2.0
provider registry.terraform.io/hashicorp/kubernetes v1.13.4
provider registry.terraform.io/hashicorp/local v2.1.0
provider registry.terraform.io/hashicorp/random v3.1.0
provider registry.terraform.io/terraform-aws-modules/http v2.4.1

Module:

Additional context

this is probably a miss configuration in the service account or aws role.
can you contact me on slack on kubernetes slack https://slack.k8s.io/ ? or devops20 https://devops20.slack.com/ ? try "@Tsachi Nimni" I'll try and help you

I have the same issue with cluster-autoscaler and EKS module:
E0810 09:43:39.872324       1 aws_manager.go:265] Failed to regenerate ASG cache: cannot autodiscover ASGs: WebIdentityErr: failed to retrieve credentials
caused by: InvalidIdentityToken: No OpenIDConnect provider found in your account for https://oidc.eks.us-west-2.amazonaws.com/id/B55C9717A50BD2366332532423443D48
	status code: 400, request id: a3d9f1bc-1fb1-4904-a34b-c63edbb1a850
F0810 09:43:39.872361       1 aws_cloud_provider.go:382] Failed to create AWS Manager: cannot autodiscover ASGs: WebIdentityErr: failed to retrieve credentials
caused by: InvalidIdentityToken: No OpenIDConnect provider found in your account for https://oidc.eks.us-west-2.amazonaws.com/id/B55C9717A50BD2366332532423443D48
	status code: 400, request id: a3d9f1bc-1fb1-4904-a34b-c63edbb1a850

I have enable_irsa = true and role associated with service account
eks.amazonaws.com/role-arn: arn:aws:iam::<redacted>:role/<redacted>

authors: happy-machine tnimni vpuchak

repo: terraform-aws-modules/terraform-aws-eks


text:
improvement: Override Tags for ASG when tag is passed in via worker launch template or config.

PR o'clock
Description
This PR brings an enhancement that allows tags that are passed into var.worker_groups_launch_template or var.worker_groups to override tags passed in via var.tags for ASGs only.
This relates to #1091
The main use case for this is where a tag is required everywhere apart from worker nodes. In this case we can pass the tag in via var.tags, but then pass it in with the worker template and set propagate_at_launch to false.
In order to ensure that the override only happens where it needs to I had to move the asg_tags for expression from locals.tf to be in the context of each worker template.
This way we don't incorrectly override tags where we haven't actually overwritten them.
I wasn't sure if you would class this as an improvement or feature, but I went with improvement, happy to change it to a "feature".
Checklist

[x] CI tests are passing
[x] README.md has been updated after any changes to variables and outputs. See https://github.com/terraform-aws-modules/terraform-aws-eks/#doc-generation

I've just merged latest master too

Thanks @huddy for your contribution. I'll make a new release as soon as fargate PR is merged.

Thanks for the very quick turnaround @barryib and once again thanks to you and other maintainers for awesome work.

authors: barryib huddy


text:
fix(iam-group-with-policies): Correct AllowManageOwnAccessKeys statement

Description
The "AWS: Self-manage credentials with MFA (My security credentials)" reference policy got updated by AWS in the docs[1] and this implements the following correction:
The AllowManageOwnAccessKeys statement allows the user to create, update, and delete their own access keys.
The user can also retrieve information about when the specified access key was last used.

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_aws_my-sec-creds-self-manage.html

Motivation and Context
Having the self-management policy up to date with AWS recommendations.
Breaking Changes
None.
How Has This Been Tested?

[ ] I have updated at least one of the examples/* to demonstrate and validate my change(s)
[x] I have tested and validated these changes using one or more of the provided examples/* projects
[ ] I have executed pre-commit run -a on my pull request

@antonbabenko , please review. Thanks in advance!

This PR is included in version 5.30.2 :tada:

authors: antonbabenko vutny

repo: terraform-aws-modules/terraform-aws-iam


text:
Hight through-put fifo queue parameters have to be enabled in this module

Is your request related to a new offering from AWS?

Is your request related to a problem? Please describe.

Describe the solution you'd like.

Describe alternatives you've considered.

Additional context

Hello, Could you please enable the high throughput fifo queue parameters in a resource so that we could able to make use of them through module call?
i.e.  deduplication_scope        = "messageGroup"
fifo_throughput_limit      = "perMessageGroupId"

Hi! Pull request is welcome.

authors: antonbabenko sidda-dandu

repo: terraform-aws-modules/terraform-aws-sqs


text:
feat(cloud_sql): add ssl_mode to PostgreSQL example

Description
Fixes #310700535
Checklist
Readiness

[X] Yes, merge this PR after it is approved
[ ] No, don't merge this PR after it is approved

Style

[ ] My sample follows the rules described for Terraform in the Effective Samples style guide
[ ] My sample follows the other requirements and best practices in the Contributing
guide

Testing

[X] I have performed tests described in the Contributing guide: https://paste.googleplex.com/6576073727279104?raw

[X] Tests pass: terraform apply
[X] Lint pass: terraform fmt check

Intended location

[X] Yes, this sample will be (or already is) included on cloud.google.com
Location(s):

[ ] No, this sample won't be included on cloud.google.com
Reason:

API enablement

[X] If the sample needs an API enabled to pass testing, I have added the service to the Test setup file

Review

[ ] If this sample adds a new directory, I have added codeowners to the CODEOWNERS file

/gcbrun

@feng-zhe - Thank you for sharing testing logs

/gcbrun

Hi @msampathkumar, thanks for reviewing the change. I saw the above
terraform-docs-samples-int-trigger (cloud-foundation-cicd) failed with --- FAIL: TestSamples/2/sqlserver_instance_private_ip (603.18s). I feel my change should not impact it. Please advice. Thanks.

Thanks @msampathkumar! Somehow the CICD still fails and I cannot find anything related to this change.

Hi @msampathkumar , any updates? Please let me know what I should to make the CICD errors go away. Thanks.

/gcbrun

Hi @msampathkumar, just a friendly ping. Thanks.

/gcbrun

Error:
Step #5 - "test-samples-2": TestSamples/2/sqlserver_instance_private_ip 2023-12-15T13:27:57Z command.go:185: Error: Unable to remove Service Networking Connection, err: Error waiting for Delete Service Networking Connection: Error code 9, message: Failed to delete connection; Producer services (e.g. CloudSQL, Cloud Memstore, etc.) are still using this connection.

I will be doing for manual testing for approval.

authors: feng-zhe msampathkumar


text:
chore(deps): update common-dev-assets digest to 2365130

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text:
Add deployable-architecture topic

Description

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:tada: This PR is included in version 8.1.6 :tada:
The release is available on:

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authors: ocofaigh terraform-ibm-modules-ops

repo: terraform-ibm-modules/terraform-ibm-cos


text:
chore(deps): update ci dependencies

This PR contains the following updates:

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digest
fe7f91b -> 1c3409f

github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper
require
patch
v1.30.5 -> v1.30.7

terraform-ibm-modules/common-pipeline-assets
action
patch
v1.21.7 -> v1.21.9

Release Notes

terraform-ibm-modules/ibmcloud-terratest-wrapper (github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper)
v1.30.7
Compare Source
Bug Fixes

deps: update gomod (#792) (f50dd6c)

v1.30.6
Compare Source
Bug Fixes

update default region to us-south (#790) (3870985)

terraform-ibm-modules/common-pipeline-assets (terraform-ibm-modules/common-pipeline-assets)
v1.21.9
Compare Source
Bug Fixes

Add time to pipeline fail issue title (#676) (29a08b8)

v1.21.8
Compare Source
Bug Fixes

pipeline fail git issue now has repo and date in title (#675) (fe98644)

Configuration
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This PR has been generated by Renovate Bot.

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text:
chore(deps): update common-dev-assets digest to 6f24289

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♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
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This PR has been generated by Renovate Bot.

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:tada: This PR is included in version 1.1.0 :tada:
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text:
chore(deps): update common-dev-assets digest to d106e3b

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♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.

[ ] If you want to rebase/retry this PR, click this checkbox.

This PR has been generated by Renovate Bot.

:tada: This PR is included in version 1.1.4 :tada:
The release is available on:

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text:
fix: add s2s auth for atracker eventstream target

Description
Issue: https://github.com/terraform-ibm-modules/terraform-ibm-observability-instances/issues/581

Release required?

[ ] No release
[ ] Patch release (x.x.X)
[x] Minor release (x.X.x)
[ ] Major release (X.x.x)

Release notes content

Run the pipeline
If the CI pipeline doesn't run when you create the PR, the PR requires a user with GitHub collaborators access to run the pipeline.
Run the CI pipeline when the PR is ready for review and you expect tests to pass. Add a comment to the PR with the following text:
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Checklist for reviewers

[ ] If relevant, a test for the change is included or updated with this PR.
[ ] If relevant, documentation for the change is included or updated with this PR.

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Use a conventional commit message to set the release level. Follow the guidelines.
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TestRunUpgradeExample is failing due to changes in the authentication method for atracker eventstream target.

    	Test:       	TestRunUpgradeExample
    	Messages:   	Resource(s) identified to be updated
    	            	Name: atracker_eventstreams_targets
    	            	Address: module.observability_instances.module.activity_tracker.ibm_atracker_target.atracker_eventstreams_targets["obs-upg-atdyig-es-target"]
    	            	Actions: [update]
    	            	DIFF:
    	            	  Before:
    	            		{"eventstreams_endpoint":"SECURE_VALUE_HIDDEN_HASH:-781375d9817072fbeee234016e2762b5026acb96b517be0d9e8a5642"}
    	            	  After:
    	            		{"eventstreams_endpoint":"SECURE_VALUE_HIDDEN_HASH:-a969af520df56a3578ba4a6cc31c219efebc569df249d90af2c531f1"}

    	            	Change Detail:
    	            	{
    	            	  "actions": [
    	            	    "update"
    	            	  ],
    	            	  "after": {
    	            	    "api_version": 2,
    	            	    "cloudlogs_endpoint": "SECURE_VALUE_HIDDEN_HASH:-569d271dfe8e452f60a74f605b790abf16872293c0e036cbe4910cdd",
    	            	    "cos_endpoint": "SECURE_VALUE_HIDDEN_HASH:-3923b08d7b052eb18b5b4f5633bdd286703ea49faaf47c2cd6f3f590",
    	            	    "cos_write_status": "SECURE_VALUE_HIDDEN_HASH:-7a5b112f990d9ba0c57dfae4a9b96143ca1f5693520f754b3b9d4d5a",
    	            	    "created": null,
    	            	    "created_at": "2024-10-15T09:49:03.763Z",
    	            	    "crn": "crn:v1:bluemix:public:atracker:eu-es:a/abac0df06b644a9cabc6e44f55b3880e::target:6eb48ed0-f6ed-4292-8166-116736fc1242",
    	            	    "encrypt_key": null,
    	            	    "encryption_key": null,
    	            	    "eventstreams_endpoint": "SECURE_VALUE_HIDDEN_HASH:-fb2c5d5039ba8483e4156a315045022aec79794624248cd1d75564a7",
    	            	    "id": "6eb48ed0-f6ed-4292-8166-116736fc1242",
    	            	    "logdna_endpoint": "SECURE_VALUE_HIDDEN_HASH:-4d738791de51d8dfb6a23d14aedc12f97d05f1b16c7b5931c1ccd1e8",
    	            	    "name": "obs-upg-atdyig-es-target",
    	            	    "region": "eu-es",
    	            	    "target_type": "event_streams",
    	            	    "updated": null,
    	            	    "updated_at": "2024-10-15T09:49:03.763Z",
    	            	    "write_status": "SECURE_VALUE_HIDDEN_HASH:-36c43efeadd458cc3c1611824565a99f6a7f8d88459ac07b9eb134dc"
    	            	  },
    	            	  "after_sensitive": {
    	            	    "cloudlogs_endpoint": "SECURE_VALUE_HIDDEN_HASH:-ef7222dcb1c2057135074c8d7bdc50670940bf63054659d67ef75f83",
    	            	    "cos_endpoint": "SECURE_VALUE_HIDDEN_HASH:-1f9ee1a63d4897ef7d6e6a8f4247cd08dd3f2f696a83d897a7c60bac",
    	            	    "cos_write_status": "SECURE_VALUE_HIDDEN_HASH:-e282313500f3256d1cef6110112ba1612f8a5d69b5aff493ff75d575",
    	            	    "eventstreams_endpoint": "SECURE_VALUE_HIDDEN_HASH:-84359a6d947319792a2edab3c234cb79a738efa35ce99bca1990942a",
    	            	    "logdna_endpoint": "SECURE_VALUE_HIDDEN_HASH:-0c5c0023aa42e98e308234f6021ed9a1cd90e11f16367d6f79d308a0",
    	            	    "write_status": "SECURE_VALUE_HIDDEN_HASH:-045c192f0eb88be23b0ba56e851fe682f9661e8ad6189e8526d6e9de"
    	            	  },
    	            	  "after_unknown": {},
    	            	  "before": {
    	            	    "api_version": 2,
    	            	    "cloudlogs_endpoint": "SECURE_VALUE_HIDDEN_HASH:-d1e8947405bf4d1b1cbce22f68b5d9ab879ce3f7b57fad7b5de827db",
    	            	    "cos_endpoint": "SECURE_VALUE_HIDDEN_HASH:-c44718006a6e82e82691ac6234f7cd88574b88f1939f520a2095214c",
    	            	    "cos_write_status": "SECURE_VALUE_HIDDEN_HASH:-7a58da3e49702ba6f99e208886389fe88f619d4caf12c33b6fe16ea7",
    	            	    "created": null,
    	            	    "created_at": "2024-10-15T09:49:03.763Z",
    	            	    "crn": "crn:v1:bluemix:public:atracker:eu-es:a/abac0df06b644a9cabc6e44f55b3880e::target:6eb48ed0-f6ed-4292-8166-116736fc1242",
    	            	    "encrypt_key": null,
    	            	    "encryption_key": null,
    	            	    "eventstreams_endpoint": "SECURE_VALUE_HIDDEN_HASH:-2c867119b951db1e9ce1565a49386652b104c820b38233aeaea6872d",
    	            	    "id": "6eb48ed0-f6ed-4292-8166-116736fc1242",
    	            	    "logdna_endpoint": "SECURE_VALUE_HIDDEN_HASH:-b7dd0e631d93673ef67b87b19e631df9818a10f716c556d689948fe9",
    	            	    "name": "obs-upg-atdyig-es-target",
    	            	    "region": "eu-es",
    	            	    "target_type": "event_streams",
    	            	    "updated": null,
    	            	    "updated_at": "2024-10-15T09:49:03.763Z",
    	            	    "write_status": "SECURE_VALUE_HIDDEN_HASH:-65104141a864f89fe3c3c7ead660e3ae752044ff5e501d5f617da296"
    	            	  },
    	            	  "before_sensitive": {
    	            	    "cloudlogs_endpoint": "SECURE_VALUE_HIDDEN_HASH:-0fb975d56c5d02406336deef828ee3a0e2e63e7acf1e5868aed58acc",
    	            	    "cos_endpoint": "SECURE_VALUE_HIDDEN_HASH:-00d224040012a6fe555dda9fcd386b62c4997fee305c2c5e5a04ed4e",
    	            	    "cos_write_status": "SECURE_VALUE_HIDDEN_HASH:-89fd08bbfa60e19f495a57e9f2407b60600fd45322f0394eadadcd76",
    	            	    "eventstreams_endpoint": "SECURE_VALUE_HIDDEN_HASH:-4a2c5719ad8fa501afa5c10389b8fcc3edd15606f8d9c0992e9b6661",
    	            	    "logdna_endpoint": "SECURE_VALUE_HIDDEN_HASH:-895871b303d044568e03ced4d2f61d14b428beeea8a729d1e3408488",
    	            	    "write_status": "SECURE_VALUE_HIDDEN_HASH:-bc5184fddba5beab98c0b1e172c9e59675aee44a01b031bd4200b145"
    	            	  }
    	            	}

/run pipeline

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:tada: This PR is included in version 3.1.0 :tada:
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text:
chore(deps): update module github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper to v1.1.3

This PR contains the following updates:

Package
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Update
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github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper
require
patch
v1.1.2 -> v1.1.3

Release Notes

terraform-ibm-modules/ibmcloud-terratest-wrapper
v1.1.3
Compare Source
Bug Fixes

added testhelper.GetBestPowerSystemsRegion (#122) (749e3f6)

Configuration
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🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.

[ ] If you want to rebase/retry this PR, click this checkbox.

This PR has been generated by Renovate Bot.

:tada: This PR is included in version 2.0.3 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


text:
chore(deps): update terraform-ibm-modules/common-pipeline-assets action to v1.16.1

This PR contains the following updates:

Package
Type
Update
Change

terraform-ibm-modules/common-pipeline-assets
action
patch
v1.16.0 -> v1.16.1

Release Notes

terraform-ibm-modules/common-pipeline-assets
v1.16.1
Compare Source
Bug Fixes

fix bug in CRA config logic (#498) (49cd1df)

Configuration
📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.

[ ] If you want to rebase/retry this PR, check this box

This PR has been generated by Renovate Bot.

/run pipeline

:tada: This PR is included in version 0.2.5 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


text:
Exclude automatic project reference dependency (it's already defined as package dependency)

Use case:
I have VS solution with several projects and packages. e.g.:

Project1.csproj (No references, Output: Project1.dll)
Project1.NuGetPackage.nuproj (References Project1, Output: Project1.nupkg)
Project2.csproj (References Project1, Output: Project2.dll)
Project2.NuGetPackage.nuproj (References Project2, Output: Project2.nupkg)

Project2 references Project1, but Project2.NuGetPackage also defines an explicit dependency on Project1.NuGetPackage (via  Item>).
After the build Project2.nupkg contains Project2.dll and Project1.dll (automatically determined as runtime needed) and explicit dependency on package Project1.nupkg.
It would be nice to have a way to exclude Project1.dll from the package Project2.nupkg.

Add a reference to Project1.NuGetPckage.nuproj from Project2.NuGetPackage.nuproj. That should cause Project2.nupkg to reference Project1.nupkg rather than import Project1.dll.

Oh, great! I wasn't think to do that. This certainly solves the issue.

repo: terrajobst/nuproj


text:
Remove unused rotationDegreesCCW argument

While doing a review for #1619, I found that the argument rotationDegreesCCW is unused -- and I don't see any downstream projects that use it. So I think it's ripe for removal.
https://github.com/terrapower/armi/blob/3d49274d8334b166e3a923770b4f4991333f6d69/armi/reactor/blocks.py#L597
https://github.com/terrapower/armi/blob/3d49274d8334b166e3a923770b4f4991333f6d69/armi/reactor/blocks.py#L1623

I checked downstream, and I can confirm that no one is using this argument.  This should is safe to remove.

Looks like this was recently removed. Ran git grep rotationDegreesCCW to confirm.

authors: albeanth andfranklin john-science

repo: terrapower/armi


text:
Move cold height expansion to bp

Description

Checklist

[x] This PR has only one purpose or idea.
[x] Tests have been added/updated to verify that the new/changed code works.

[x] The code style follows good practices.
[x] The commit message(s) follow good practices.

[x] The release notes (location doc/release/0.X.rst) are up-to-date with any bug fixes or new features.
[x] The documentation is still up-to-date in the doc folder.
[x] The dependencies are still up-to-date in setup.py.

Load state will be the same since we use what’s in the db for that and only expand blueprints assems with load state (both before and after)
but o=armi.init will be much faster

The new implementation should have the exact same output/methodology as the old, but the implementation is simpler and faster, which is nice.

I'm not convinced the axial expansion logic belongs in the blueprints.
What about non-axial reactors?  Tony and I worked over a couple of pull requests to pull all references to axial expansion OUT of the blueprints, and just have references to the logic.
What is the benefit of having our default blueprint logic aware of axial expansion?  (Please note that two thirds of the projects most heavily using ARMI don't have axial layouts or assemblies. And even at TerraPower, one of our two reactors won't ever need to use ANY axial expansion logic.
Thoughts?

@john-science I absolutely think calls should to happen here rather than processCoreLoading. What's currently happening in processCoreLoading is

if its a db load just expand the blueprints since heights are params and correct
if its a first initialization expand both blueprints and assemblies in the reactor. BUT the only reason we have to expand the assems in the reactor is we failed to expand the blueprints before we deep-copied them to populate the reactor. Its a tremendous waste to deepcopy a whole reactor of assemblies that have incorrect masses and axial heights. The reason that we got here is there was a bit of debate about what he blueprints should store. what we landed on is that it should store axially expanded assemblies and it should be the same at each statepoint since its not in the database. therefore doing it here is the least expensive/most clear.

I think most of the logic is out of the blueprints... right now we are just checking some settings and calling outside methods/functions that update the state, but i would be happy to move the remainder out as well and have it be a 1 line call as shown in the comment..
Where would you want this stuff to live?

I do want the call to be front this location though so that the assemblies in blueprints exist at the wrong dimensions and densities as short a time as possible.

@john-science, I moved as much of the logic as possible out of blueprints, and added comments to hopefully communicate better what is happening and why its needed useful.
If you have more comments could you please be specific about what you want done? I feel like i am guessing/stabbing in the dark here... I could make better use of the time i have to update the code if the comments were a little more specific/actionable. Thanks!

@albeanth here is what time looks like on small test reactor....
before
no expand = 24.5 sec
expand cold to hot = 49.5 sec
after
no expand = 21.2 sec
expand cold to hot = 25.9 sec
some reactors take more like 5 minutes to load, so folks should notice a difference.
import armi

armi.configure()

from armi.reactor.tests import test_reactors
import timeit

from armi import context

context.Mode.setMode(context.Mode.BATCH)  # disable yes/no CLI

hot = timeit.timeit(
    'test_reactors.loadTestReactor(customSettings={"inputHeightsConsideredHot": True})',
    setup="from armi.reactor.tests import test_reactors",
    number=10,
)

cold = timeit.timeit(
    'test_reactors.loadTestReactor(customSettings={"inputHeightsConsideredHot": False})',
    setup="from armi.reactor.tests import test_reactors",
    number=10,
)

print(hot, cold)

authors: john-science onufer


text:
Shem energy structure

This pull adds in the SHEM361 neutron energy group structure, generalizes the function to make a group structure that snaps to the boundaries of the ANL2081 structure to an arbitrary fine group structure and uses that function to make a 33 group structure -- SHEM33_361 -- that is like ANL33, but snapped to the boundaries of SHEM361.
This addresses issue https://github.com/terrapower/armi/issues/237

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.Tommy Cisneros seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.Tommy Cisneros seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.

authors: CLAassistant sombrereau


text:
Thermal expansion update

Description
An update to thermal expansion within the axial expansion changer:

demonstrates conservation with thermal expansion/contraction at both hot and cold temperatures

the thermal expansion factors (and block heights they were relative to) previously were incorrect. This PR resolves this.

removes unused class methods that are very heavy to unit test. They just aren't worth keeping around and lead to more confusion then they're worth.
make manageCoreMesh public, so that users can unify the axial mesh that results from expanding a core (if desired and if detailedAxialExpansion: False.

Checklist

[x] The code is understandable and maintainable to people beyond the author.
[x] Tests have been added/updated to verify that the new or changed code works.
[x] There is no commented out code in this PR.
[x] The commit message follows good practices.
[x] All docstrings are still up-to-date with these changes.

If user exposed functionality was added/changed:

[x] Documentation added/updated in the doc folder.

Will update the "implicitly" included list in #790  once this gets merged.

Going to wait for @john-science to review before merging. This seems to work for thermal contract and expansion now.

The change itself seems solid enough.
I'm a little wary of keeping a BIG dictionary around with every Component in the Reactor in it.  Maybe it's necessary, but we certainly don't want to be duplicating that much memory.
More importantly, since this is essentially a shadow Reactor object (an entire copy of all of the Reactor components, just stored differently), I am a little concerned that this will lead to wild complications. In that any change elsewhere in the code will have to be mimicked here.
For instance, I mention moving an assembly to the Spent Fuel Pool (SFP).  When someone does that (and they do), what happens to these dictionaries?  Do we need to go find all places in ARMI and the downstream repos that deal with SFPs and make changes to support this shadow Reactor?
I'm just talking here. This might be a smaller feature than I think it is.

I'm a little wary of keeping a BIG dictionary around with every Component in the Reactor in it. Maybe it's necessary, but this is essentially a shadow Reactor object (an entire copy of all of the Reactor components, just stored differently).

This is a valid concern, I think. The purpose of these dictionaries is to just store heights and temps, but by using the Components as the keys, this probably is way heavier than it needs to be. I wonder if there is a way that we can maintain the same behavior but with a lighter key...? 🤔
Once consolation is that this method isn't called very often. The dictionaries are initialized each time the AxialExpansionChanger is fired up, but they aren't filled unless updateComponentTempsBy1DTempField is called... But that certainly doesn't excuse it...

Go for the merge if you are good @john-science! Thanks for opening that issue @albeanth

authors: albeanth jakehader john-science


text:
SW-3535 Support per-planting-zone plot counts

For each planting zone, keep track of how many permanent and temporary monitoring
plots will be needed for the next observation.
Ordinarily, these numbers will be calculated based on statistical information
about the planting zone, expressed as variance and error margin along with a value
that represents the desired confidence level (Student's t). The confidence level
is expected to stay the same across all planting sites, so it is initialized to
the correct value when a shapefile is imported. The other two values will be
supplied by the science team prior to the first observation of a planting site.
Those numbers are also stored on a per-planting-zone basis, and can be edited in
the admin UI. The admin UI uses those numbers to calculate the number of permanent
monitoring plots, which is then used to calculate the number of temporary plots.
These calculations are done client-side in the admin UI so as to allow the admin
to manually adjust the plot counts before saving if needed. The plot counts can
also be entered directly without using the formula at all.
The map in the admin UI shows the correct number of permanent clusters for each
planting zone rather than a hardwired number of them.

Current dependencies on/for this PR:

main

PR #1114

PR #1115   👈

This comment was auto-generated by Graphite.

repo: terraware/terraware-server


text:
SW-2598 Fix single batch flow switching to bulk, also fix inventory create view styles

Before

After

Looks nice 👍

Thanks 🙏

repo: terraware/terraware-web


text:
Start section for Earth

In this particular section just enter information that would be talked about the earth as one of the planets in the solar system, comparing and/or contrasting with other planets, or basic facts about size, distance from sun, etc. that would be in the other planet sections.

This issue isn't needed anymore.

repo: terrifricker/recall-group


text:
Error messages when using j,k,l,h after selection multiple words

I press Control-n several times to select a word, the I press any of the movement keys and I see this:
Error while processing function 59_apply_user_input_next..385
E488: Trailing characters
E488: Trailing characters
but the movement works. This is what I have in my .vim directory:
/Users/julian/.vim
├── after
│   └── syntax
│       ├── haml.vim
│       └── html.vim
├── autoload
│   ├── pathogen.vim
│   ├── rails.vim
│   └── zencoding.vim
├── bundle
│   ├── ctrlp.vim
│   │   ├── autoload
│   │   │   ├── ctrlp
│   │   │   │   ├── bookmarkdir.vim
│   │   │   │   ├── buffertag.vim
│   │   │   │   ├── changes.vim
│   │   │   │   ├── dir.vim
│   │   │   │   ├── line.vim
│   │   │   │   ├── mixed.vim
│   │   │   │   ├── mrufiles.vim
│   │   │   │   ├── quickfix.vim
│   │   │   │   ├── rtscript.vim
│   │   │   │   ├── tag.vim
│   │   │   │   ├── undo.vim
│   │   │   │   └── utils.vim
│   │   │   └── ctrlp.vim
│   │   ├── plugin
│   │   │   └── ctrlp.vim
│   │   └── readme.md
│   ├── desert.vim
│   │   ├── README
│   │   └── desert.vim
│   ├── nerdtree
│   │   ├── README.markdown
│   │   ├── autoload
│   │   │   └── nerdtree.vim
│   │   ├── nerdtree_plugin
│   │   │   ├── exec_menuitem.vim
│   │   │   └── fs_menu.vim
│   │   ├── plugin
│   │   │   ├── NERD_tree.vim
│   │   │   └── nerdtree
│   │   │       ├── bookmark.vim
│   │   │       ├── creator.vim
│   │   │       ├── key_map.vim
│   │   │       ├── menu_controller.vim
│   │   │       ├── menu_item.vim
│   │   │       ├── opener.vim
│   │   │       ├── path.vim
│   │   │       ├── tree_dir_node.vim
│   │   │       └── tree_file_node.vim
│   │   └── syntax
│   │       └── nerdtree.vim
│   ├── supertab
│   │   ├── Makefile
│   │   ├── README.rst
│   │   └── plugin
│   │       └── supertab.vim
│   ├── vim-fugitive
│   │   ├── README.markdown
│   │   └── plugin
│   │       └── fugitive.vim
│   ├── vim-multiple-cursors
│   │   ├── CHANGELOG.md
│   │   ├── MIT-LICENSE.txt
│   │   ├── README.md
│   │   ├── assets
│   │   │   ├── example1.gif
│   │   │   ├── example2.gif
│   │   │   └── example3.gif
│   │   ├── autoload
│   │   │   └── multiple_cursors.vim
│   │   ├── doc
│   │   │   ├── multiple_cursors.txt
│   │   │   └── tags
│   │   └── plugin
│   │       └── multiple_cursors.vim
│   ├── vim-sensible
│   │   ├── README.markdown
│   │   └── plugin
│   │       └── sensible.vim
│   └── vim-surround
│       ├── README.markdown
│       └── plugin
│           └── surround.vim
├── compiler
│   └── coffee.vim
├── doc
│   ├── coffee-script.txt
│   ├── rails.txt
│   ├── tags
│   └── zencoding.txt
├── ftdetect
│   ├── coffee.vim
│   └── eco.vim
├── ftplugin
│   └── coffee.vim
├── indent
│   └── coffee.vim
├── plugin
│   ├── rails.vim
│   └── zencoding.vim
└── syntax
├── coffee.vim
└── eco.vim
35 directories, 69 files
And this is my vimrc
if v:progname =~? "evim"
finish
endif
set nocompatible
set backspace=indent,eol,start
if has("vms")
set nobackup		" do not keep a backup file, use versions instead
else
set backup		" keep a backup file
endif
set history=1000		" keep 50 lines of command line history
set ruler		" show the cursor position all the time
set showcmd		" display incomplete commands
set incsearch		" do incremental searching
map Q gq
inoremap  u
syntax on
set hlsearch
augroup vimrcEx
au!
autocmd FileType text setlocal textwidth=78
autocmd BufReadPost *
\ if line("'"") > 1 && line("'"") <= line("$") |
\   exe "normal! g`"" |
\ endif
augroup END
set autoindent		" always set autoindenting on
if !exists(":DiffOrig")
command DiffOrig vert new | set bt=nofile | r # | 0d_ | diffthis
\ | wincmd p | diffthis
endif
au BufRead,BufNewFile *.rabl setf ruby
set smartindent
set tabstop=2
set shiftwidth=2
set expandtab
set colorcolumn=120
set number
hi CursorLine   cterm=NONE ctermbg=darkred ctermfg=white guibg=darkred guifg=white
hi CursorColumn cterm=NONE ctermbg=darkred ctermfg=white guibg=darkred guifg=white
nnoremap c :set cursorline! cursorcolumn!
" Pathogen
call pathogen#infect()
call pathogen#helptags()
set statusline=%<\ %n:%f\ %m%r%y%=%-35.(line:\ %l\ of\ %L,\ col:\ %c%V\ (%P)%)
set mousehide
set ignorecase
set nowrap
set cursorline
set softtabstop=2
set background=dark
colorscheme desert
nmap : :%s/:([a-z_0-9]+)\s+=>/\1:/g
set autoread " Auto-reload buffers when file changed on disk
set backupdir=~/.vim_backup    " where to put backup files.
set directory=~/.vim_temp      " where to put swap files.
let mapleader=","

Seems like problem has come back.
I press  several times to select a word, the I go to the insert mode ("I") and write any symbol. Error message:

line   18:
E488: Trailing characters
Error detected while processing function 90_process_user_input:

I'm using internal OSX vim.
OSX version: 1011.4
My vim config

authors: cheshir julianvargasalvarez

repo: terryma/vim-multiple-cursors


text:
chore(main): release siyuan-blog 2.3.0

:robot: A new release will be created
2.3.0 (2024-11-05)
Features

support pink-room theme (8bc3cc7)
support Trends theme (36c6749)
support Tsundoku theme (2df8df8)
update Savor to 4.2.3, siyuan to 3.1.10 (453460f)
update Zhihu to 0.1.2 (7c9143c)

Bug Fixes

Zhihu theme code block (bda512f)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/terwer/siyuan-plugin-blog/releases/tag/siyuan-blog-v2.3.0 :sunflower:

repo: terwer/siyuan-plugin-blog


text:
jg updated

fixed typos and added jg to wildfire

Link to funding doesn't work properly

repo: tesla-cu/website


text:
Error 'Icons should be specified in the form "prefix:name" for dictionary value @ data['icon']' when processing MQTT discovery message topic

Hello,
I have this error when I restart HAOS :

Enregistreur: homeassistant.components.mqtt.mixins
Source: components/mqtt/mixins.py:164
intégration: MQTT (documentation, problèmes)
S'est produit pour la première fois: 08:33:19 (1 occurrences)
Dernier enregistrement: 08:33:19
Error 'Icons should be specified in the form "prefix:name" for dictionary value @ data['icon']' when processing MQTT discovery message topic: 'homeassistant/button/tesla_ble_xxxxxxxxxxxxxxxxx/drive/config', message: '{'command_topic': 'tesla_ble/xxxxxxxxxxxxxxxxx/command', 'device': {'identifiers': ['tesla_ble_xxxxxxxxxxxxxxxxx'], 'manufacturer': 'tesla-local-control', 'model': 'Tesla_BLE', 'sw_version': '0.2.0', 'name': 'Tesla_BLE_xxxxxxxxxxxxxxxxx'}, 'icon': 'car-wireless', 'payload_press': 'drive', 'qos': '1', 'unique_id': 'tesla_ble_xxxxxxxxxxxxxxxxx_drive', 'name': 'mdi:car-wireless,Remote start car', 'platform': 'mqtt'}'

Core : 2024.8.3
Supervisor : 2024.09.1
Operating System : 13.1
Interface utilisateur : 20240809.0

Thanks, yes we've seen this issue.
Should not prevent the addon to start, but to be fixed indeed.

authors: HiFuGa raphmur


text:
Create Card component

Create an angular component that draws a card on the SVG canvas.

The Card component will handle rendering the card, but there should also be a CardModel class that the Card uses as it's model. (class names TBD)

I'd probably reverse the naming a bit. I'd probably say something more like "CanvasRenderer" for the logic you built in the demo and "Card" for the model API that wraps our raw data.